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Master'sElectromobility, Batteries & Hydrogen Applications open

Master’s in Advanced BMS and Diagnostics/Prognostics Engineering

Online master's programme of 1,900 hours in advanced BMS engineering and diagnostics/prognostics: 12 modules covering BMS functions and hardware, battery models, SOC, SOH and SOP estimation, cell balancing, fault diagnosis, prognostics and remaining useful life, BMS software, functional safety and cybersecurity, and HIL validation, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Design BMS hardware and software architectures.

02

Implement algorithms for estimating state of charge, health and power.

03

Develop diagnostics for cell, sensor and insulation faults.

04

Apply prognostics and remaining useful life techniques.

05

Integrate functional safety and cybersecurity into the BMS.

06

Validate the BMS through simulation and hardware in the loop.

07

Develop an advanced BMS with diagnostic and prognostic functions.

Profile

Who it's for

  • BMS engineers looking to move into advanced estimation, diagnostics and prognostics.
  • Battery algorithm engineers who want to deepen their knowledge of state estimation and ageing.
  • Embedded software engineers working on battery management electronics.
  • Battery data scientists applying analytics and machine learning to cell and fleet data.
  • Functional safety engineers involved in battery systems.
  • HIL validation engineers testing battery management systems.
  • Engineering or science graduates aiming to join electric vehicle manufacturers.
Standards & tools

What you'll work with

  • ISO 26262-5 (road vehicle functional safety, hardware level)
  • ISO 26262-6 (road vehicle functional safety, software level)
  • IEC 62619 (safety of industrial lithium batteries)
  • ISO 11898-1 (CAN bus data link layer)
  • ISO 14229-1 (Unified Diagnostic Services)
  • Regulation (EU) 2023/1542 (EU Batteries Regulation)
  • UL 1973 (batteries for stationary and motive applications)
  • ISO/SAE 21434 (road vehicle cybersecurity engineering)
  • MATLAB/Simulink
  • dSPACE
  • PyBaMM
  • Python with scikit-learn
  • Vector CANoe
Syllabus

12 modules · 1900 h

  1. 1.1Cell monitoring
  2. 1.2Cell and pack protection
  3. 1.3State estimation
  4. 1.4Cell balancing in the pack
  5. 1.5Thermal management of the assembly
  6. 1.6Communication with the vehicle
  7. 1.7Data logging
  8. 1.8Requirements of the EU Batteries Regulation
  9. 1.9BMS in different applications
  10. 1.10Case study: functional specification of a BMS
  1. 2.1Centralised and distributed topologies
  2. 2.2Cell measurement circuits
  3. 2.3Measurement accuracy
  4. 2.4Current measurement with shunt and Hall effect
  5. 2.5Galvanic isolation between stages
  6. 2.6Daisy-chain communication
  7. 2.7Wireless BMS
  8. 2.8Power supply and low consumption
  9. 2.9Design for EMC
  10. 2.10Lab: characterisation of an analogue front end
  1. 3.1Equivalent circuit models
  2. 3.2Open-circuit voltage curve
  3. 3.3Voltage hysteresis
  4. 3.4Temperature dependence
  5. 3.5Parameter identification
  6. 3.6Reduced-order electrochemical models
  7. 3.7Simplified thermal models
  8. 3.8Complexity versus accuracy
  9. 3.9Model validation
  10. 3.10Practical: identification of a 2RC model
  1. 4.1Coulomb counting
  2. 4.2OCV correction
  3. 4.3Extended Kalman filter
  4. 4.4Unscented Kalman filter
  5. 4.5Non-linear observers
  6. 4.6Estimation with neural networks
  7. 4.7Robustness to sensor errors
  8. 4.8Estimation in LFP
  9. 4.9Accuracy validation
  10. 4.10Lab: EKF for SOC in Simulink
  1. 5.1Capacity fade
  2. 5.2Resistance increase
  3. 5.3Joint parameter estimation
  4. 5.4Incremental capacity analysis
  5. 5.5On-board impedance spectroscopy
  6. 5.6State of power
  7. 5.7Dynamic current limits
  8. 5.8State of energy
  9. 5.9Communication to the user
  10. 5.10Workshop: SOH estimation with recursive least squares
  1. 6.1Cell-to-cell imbalances
  2. 6.2Passive cell balancing
  3. 6.3Active cell balancing
  4. 6.4Balancing strategies
  5. 6.5Balancing during charging and at rest
  6. 6.6Impact on usable capacity
  7. 6.7Thermal dissipation from balancing
  8. 6.8Decision algorithms
  9. 6.9Validation of results
  10. 6.10Practical: simulation of active balancing
  1. 7.1Sensor faults
  2. 7.2Internal short circuit in cells
  3. 7.3Detection of lithium plating
  4. 7.4Connection faults
  5. 7.5Insulation faults
  6. 7.6Early detection of thermal runaway
  7. 7.7Model-based diagnosis
  8. 7.8Data-driven diagnosis
  9. 7.9Fault codes
  10. 7.10Case study: detection of an internal short circuit
  1. 8.1Prognostics concepts
  2. 8.2Physics-based ageing models
  3. 8.3Empirical ageing models
  4. 8.4Machine learning for remaining useful life
  5. 8.5Prediction uncertainty
  6. 8.6Laboratory and field data
  7. 8.7Battery digital twin
  8. 8.8Warranty and prognostics
  9. 8.9Second-life batteries
  10. 8.10Lab: remaining useful life prediction with Python
  1. 9.1Software architecture
  2. 9.2AUTOSAR software architecture
  3. 9.3Model-based development
  4. 9.4Code generation
  5. 9.5Calibration of system parameters
  6. 9.6Diagnostics via the UDS protocol
  7. 9.7Remote software updates
  8. 9.8Software unit testing
  9. 9.9Configuration management
  10. 9.10Practical: integration of an algorithm into embedded software
  1. 10.1BMS hazard analysis
  2. 10.2Safety goals
  3. 10.3ASIL integrity requirements
  4. 10.4Redundant measurement architectures
  5. 10.5Hardware failure analysis
  6. 10.6Safe software development
  7. 10.7BMS cybersecurity
  8. 10.8Battery data protection
  9. 10.9Technical documentation of the process
  10. 10.10Workshop: safety concept for a BMS
  1. 11.1Closed-loop simulation
  2. 11.2Hardware in the loop
  3. 11.3Cell emulators
  4. 11.4Fault injection
  5. 11.5Tests with real batteries
  6. 11.6In-vehicle validation
  7. 11.7Requirements coverage
  8. 11.8Test automation
  9. 11.9Validation reports
  10. 11.10Case study: HIL campaign for a BMS
  1. 12.1BMS specification
  2. 12.2Hardware architecture
  3. 12.3Battery model
  4. 12.4State estimators
  5. 12.5Fault diagnosis
  6. 12.6Service life prognostics
  7. 12.7Safety concept
  8. 12.8Validation of results
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable materials, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so the programme can be combined with full-time work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB/Simulink, dSPACE, PyBaMM, Python with scikit-learn and Vector CANoe, in free, academic or evaluation versions. Practical work is based on real procedures: implementing an extended Kalman filter for state of charge, estimating state of health with fleet data, and validating a BMS in hardware in the loop with fault injection. Assessment is continuous for each module, plus the final Master's Thesis.

Standards covered: ISO 26262-5, ISO 26262-6, IEC 62619, ISO 11898-1, ISO 14229-1, Regulation (EU) 2023/1542, UL 1973, ISO/SAE 21434.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 26262-5, ISO 26262-6 and IEC 62619 applied to real cases, not as a theoretical annex.
Industry software:MATLAB/Simulink, dSPACE, PyBaMM, Python with scikit-learn and Vector CANoe, in free, academic or evaluation versions.
Master's Thesis with defence:an advanced BMS with diagnostic and prognostic functions, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Specify the BMS
Define the functional specification and hardware architecture of the battery management system.
Identify the battery model
Build and validate an equivalent circuit model for state estimation.
Implement state estimators
Develop SOC, SOH and SOP estimators and check their accuracy.
Diagnose and predict
Add fault diagnosis and remaining useful life prognostics, backed by a safety concept.
Master's Thesis
An advanced BMS with diagnostic and prognostic functions, defended before a panel.
Career opportunities

Where this takes you

BMS engineerBattery algorithm engineerEmbedded software engineerBattery data scientistFunctional safety engineerHIL validation engineer
Admissions & scholarships

Requirements

  • A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • An internet connection and a computer capable of running the programme software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

Apply now Scholarships
FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with MATLAB/Simulink, dSPACE, PyBaMM, Python with scikit-learn and Vector CANoe, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the programme software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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